@inproceedings{wang-etal-2026-agsynth,
title = "{A}g{S}ynth: A Multi-Agent Reverse Synthesis Framework for Multimodal Mathematical Reasoning Data Generation",
author = "Wang, Yaxuan and
Zeng, Lingfeng and
Hu, Jiatu and
Lin, Guojie and
Yang, Boyu and
Zhao, Bing and
Qu, Lin and
Wei, hu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2073/",
pages = "44135--44148",
ISBN = "979-8-89176-462-0",
abstract = "Multimodal large language models show significant potential in mathematical reasoning, yet they remain constrained by a scarcity of high-quality training data for visually grounded problems. Existing approaches commonly suffer from narrow coverage, image-text inconsistency, and heavy imitation of seed problems. To address these limitations, this paper proposes AgSynth, a multi-agent collaborative reverse synthesis framework. It first constructs solution logic programmatically and renders the corresponding images, then generates problem statements backward from the visual facts, thereby embedding answer correctness and image-text consistency directly into the generation pipeline. Meanwhile, AgSynth distills the core competencies tested in seed problems and performs divergent reconstruction, enabling synthesized problems to be substantially novel in both structure and visual presentation. Based on this framework, we construct the AgSynth-10K dataset, comprising $10{,}477$ multimodal mathematical reasoning problems that have undergone two-tier quality control, spanning four major domains: geometry, algebra, combinatorics, and number theory. Experiments demonstrate that reinforcement learning training on AgSynth-10K significantly improves model performance on reasoning-intensive benchmarks (WeMath $+7.42$, LogicVista$+9.15$), validating the effectiveness of this traceable synthesis paradigm. The framework is publicly available at https://github.com/Yaxuan7690/AgSynth."
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<abstract>Multimodal large language models show significant potential in mathematical reasoning, yet they remain constrained by a scarcity of high-quality training data for visually grounded problems. Existing approaches commonly suffer from narrow coverage, image-text inconsistency, and heavy imitation of seed problems. To address these limitations, this paper proposes AgSynth, a multi-agent collaborative reverse synthesis framework. It first constructs solution logic programmatically and renders the corresponding images, then generates problem statements backward from the visual facts, thereby embedding answer correctness and image-text consistency directly into the generation pipeline. Meanwhile, AgSynth distills the core competencies tested in seed problems and performs divergent reconstruction, enabling synthesized problems to be substantially novel in both structure and visual presentation. Based on this framework, we construct the AgSynth-10K dataset, comprising 10,477 multimodal mathematical reasoning problems that have undergone two-tier quality control, spanning four major domains: geometry, algebra, combinatorics, and number theory. Experiments demonstrate that reinforcement learning training on AgSynth-10K significantly improves model performance on reasoning-intensive benchmarks (WeMath +7.42, LogicVista+9.15), validating the effectiveness of this traceable synthesis paradigm. The framework is publicly available at https://github.com/Yaxuan7690/AgSynth.</abstract>
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%0 Conference Proceedings
%T AgSynth: A Multi-Agent Reverse Synthesis Framework for Multimodal Mathematical Reasoning Data Generation
%A Wang, Yaxuan
%A Zeng, Lingfeng
%A Hu, Jiatu
%A Lin, Guojie
%A Yang, Boyu
%A Zhao, Bing
%A Qu, Lin
%A Wei, hu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-agsynth
%X Multimodal large language models show significant potential in mathematical reasoning, yet they remain constrained by a scarcity of high-quality training data for visually grounded problems. Existing approaches commonly suffer from narrow coverage, image-text inconsistency, and heavy imitation of seed problems. To address these limitations, this paper proposes AgSynth, a multi-agent collaborative reverse synthesis framework. It first constructs solution logic programmatically and renders the corresponding images, then generates problem statements backward from the visual facts, thereby embedding answer correctness and image-text consistency directly into the generation pipeline. Meanwhile, AgSynth distills the core competencies tested in seed problems and performs divergent reconstruction, enabling synthesized problems to be substantially novel in both structure and visual presentation. Based on this framework, we construct the AgSynth-10K dataset, comprising 10,477 multimodal mathematical reasoning problems that have undergone two-tier quality control, spanning four major domains: geometry, algebra, combinatorics, and number theory. Experiments demonstrate that reinforcement learning training on AgSynth-10K significantly improves model performance on reasoning-intensive benchmarks (WeMath +7.42, LogicVista+9.15), validating the effectiveness of this traceable synthesis paradigm. The framework is publicly available at https://github.com/Yaxuan7690/AgSynth.
%U https://aclanthology.org/2026.findings-emnlp.2073/
%P 44135-44148
Markdown (Informal)
[AgSynth: A Multi-Agent Reverse Synthesis Framework for Multimodal Mathematical Reasoning Data Generation](https://aclanthology.org/2026.findings-emnlp.2073/) (Wang et al., Findings 2026)
ACL
- Yaxuan Wang, Lingfeng Zeng, Jiatu Hu, Guojie Lin, Boyu Yang, Bing Zhao, Lin Qu, and hu Wei. 2026. AgSynth: A Multi-Agent Reverse Synthesis Framework for Multimodal Mathematical Reasoning Data Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 44135–44148, Budapest, Hungary. Association for Computational Linguistics.